...Anar Nurizada; Rohit Dhaipule; Zhijie Lyu; Anurag Purwar In recent years, there has been a strong interest in applying machine learning techniques to path synthesis of linkage mechanisms. However, progress has been stymied due to a scarcity of high-quality datasets. In this article, we present...

...Kevin Ferguson; Yu-hsuan Chen; Yiming Chen; Andrew Gillman; James Hardin; Levent Burak Kara Machine-learned surrogate models to accelerate lengthy computer simulations are becoming increasingly important as engineers look to streamline the product design cycle. In many cases, these approaches offer...

...: ankush.kapoor@unsw.edu.au coronary stent design computer-aided design design optimization design representation machine learning product design Algorithm 1 Surrogate assisted optimization framework Require: Evaluated LHS design solutions X l , objective function value of evaluated...

... Abstract Figure surrogate modeling image-based surrogates deep neural networks dimensionality reduction artificial intelligence data-driven design machine learning robust design uncertainty modeling Office of Energy Efficiency and Renewable Energy 10.13039/100006134 DE...

... automation generative design machine learning Division of Civil, Mechanical and Manufacturing Innovation 10.13039/100000147 2245299 In computer graphics and the computer-aided design (CAD)/computer-aided manufacturing (CAM) industry, parametric smooth curves (e.g., Bézier, B-spline...

... Graphical Abstract Figure porous metamaterial graph neural network variational graph autoencoder design optimization generative design connectivity data-driven design design of engineered materials system machine learning Division of Civil, Mechanical and Manufacturing Innovation...

... , A. , and Tan , C. , 2020 , “ Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations ,” ACM Conference on Fairness, Accountability, and Transparancy , Barcelona, Spain , Jan. 27–30 , pp. 607 – 617 . [12] Poyiadzi , R. , Sokol , K. , Santos-Rodriguez , R...

...Milad Habibi; Shai Bernard; Jun Wang; Mark Fuge When performing time-intensive optimization tasks, such as those in topology or shape optimization, researchers have turned to machine-learned inverse design (ID) methods—i.e., predicting the optimized geometry from input conditions—to replace or warm...

... customizable sit-to-stand (STS) mechanical device by integrating rigid body kinematics with machine learning. Unlike traditional mechanism synthesis approaches that primarily focus on limited functional requirements, such as path or motion generation, our proposed design pipeline efficiently generates a large...

... human–computer interaction transparency robustness fairness experimental study artificial intelligence cognitive-based design design theory and methodology machine learning References [1] Asan , O. , Bayrak , A. E. , and Choudhury , A. , 2020 , “ Artificial...

... constrained multi-objective optimization robustness metrics computational efficiency artificial intelligence data-driven design design automation design for manufacturing design integration design of multiscale systems design optimization machine learning metamodeling Designing...

... and concept generation machine learning Understanding and analyzing design requirements is a common challenge in design. Designers are often required to explore a range of potential ideas before choosing the appropriate and innovative solution to implement. In this process, early conceptualization...

... in the J ournal of M echanical D esign . 04 01 2024 22 04 2024 06 05 2024 03 06 2024 Graphical Abstract Figure design process machine learning Division of Civil, Mechanical and Manufacturing Innovation 10.13039/100000147 2322853 Design can...

... 09 04 2024 Graphical Abstract Figure data-driven design design of experiments design optimization design process machine learning simulation-based design Advanced Research Projects Agency - Energy 10.13039/100006133 DE-AR0001427 National Science Foundation...

...). The concept of latent crossover enables the integration of evolutionary algorithms and machine learning methods. In our future work, we plan to incorporate various types of evolutionary algorithms other than RCGAs, as well as VAE-based advanced machine learning methods into the proposed framework. In addition...

... 18 03 2024 Graphical Abstract Figure Bayesian optimization optimization-under-uncertainty efficient robust global optimization hypervolume expected improvement crash constraints Bayesian classification design optimization machine learning metamodeling multi-objective...

... problem. artificial intelligence design methodology design optimization machine learning metamodeling multi-objective optimization systems design Group behavior is widespread with phenomena like ant colonies, fish swarms, bird flights, and so on. The industry has been inspired...

... of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly...

... 05 03 2024 Graphical Abstract Figure multi-fidelity surrogate neural network machine learning mapping model different input spaces artificial intelligence computer-aided engineering metamodeling Output data from engineering systems, whether observed or predicted...

... Networks (IJCNN) , Padua, Italy , July 18–23 , pp. 1 – 8 . [5] Pascanu , R. , Mikolov , T. , and Bengio , Y. , 2013 , “ On the Difficulty of Training Recurrent Neural Networks ,” Proceedings of the 30th International Conference on Machine Learning , Atlanta, GA , June 17–19 , S...